Add the practice

README.md

@@ -19,9 +19,9 @@ base_model:
 ---
 # [TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting](https://arxiv.org/abs/2310.04948)
 
-[![preprint](https://img.shields.io/static/v1?label=arXiv&message=2310.04948&color=B31B1B&logo=arXiv)](https://arxiv.org/pdf/2310.04948) [![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/Melady/TEMPO) [![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![preprint](https://img.shields.io/static/v1?label=arXiv&message=2310.04948&color=B31B1B&logo=arXiv)](https://arxiv.org/pdf/2310.04948) 
 
-![TEMPO_logo](pics/TEMPO_logo.png)
+![TEMPO_logo|50%](pics/TEMPO_logo.png)
 
 
 
@@ -43,27 +43,27 @@ We use the following Colab page to show the demo of building the customer datase
 
 
 
-# Practice
+# 🔧 Hands-on: Using Foundation Model
 
-## Download the repo
+## 1. Download the repo
 
 ```
 git clone [email protected]:DC-research/TEMPO.git
 ```
 
-## [Optional] Download the model and config file via commands
+## 2. [Optional] Download the model and config file via commands
 ```
 huggingface-cli download Melady/TEMPO config.json --local-dir ./TEMPO/TEMPO_checkpoints
 ```
 ```
-huggingface-cli download Melady/TEMPO TEMPO-80M_v2.pth --local-dir ./TEMPO/TEMPO_checkpoints
+huggingface-cli download Melady/TEMPO TEMPO-80M_v1.pth --local-dir ./TEMPO/TEMPO_checkpoints
 ```
 
 ```
-!huggingface-cli download Melady/TEMPO TEMPO-80M_v1.pth --local-dir ./TEMPO/TEMPO_checkpoints
+huggingface-cli download Melady/TEMPO TEMPO-80M_v2.pth --local-dir ./TEMPO/TEMPO_checkpoints
 ```
 
-## Build the environment
+## 3. Build the environment
 
 ```
 conda create -n tempo python=3.8
@@ -78,7 +78,7 @@ cd TEMPO
 pip install -r requirements.txt
 ```
 
-## Script Demo
+## 4. Script Demo
 
 A streamlining example showing how to perform forecasting using TEMPO:
 
@@ -107,31 +107,31 @@ print(predicted_values)
 ```
 
 
-## Online demo:
+## 5. Online demo
 
 Please try our foundation model demo [[here]](https://4171a8a7484b3e9148.gradio.live).
 
 ![TEMPO_demo.jpg](pics/TEMPO_demo.jpg)
 
 
-## Practice on your end
+# 🔨 Advanced Practice: Full Training Workflow!
 
 We also updated our models on HuggingFace: [[Melady/TEMPO]](https://huggingface.co/Melady/TEMPO).
 
 
 
-### Get Data
+## 1. Get Data
 
    Download the data from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy), and place the downloaded data in the folder`./dataset`. You can also download the STL results from [[Google Drive]](https://drive.google.com/file/d/1gWliIGDDSi2itUAvYaRgACru18j753Kw/view?usp=sharing), and place the downloaded data in the folder`./stl`.
 
-### Run TEMPO
+## 2. Run Scripts
 
-### Pre-Training Stage
+### 2.1 Pre-Training Stage
 ```
 bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather].sh
 ```
 
-### Test/ Inference Stage
+### 2.2 Test/ Inference Stage
 
 After training, we can test TEMPO model under the zero-shot setting:
 
@@ -142,11 +142,11 @@ bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather]_test.sh
 ![TEMPO-results](pics/results.jpg)
 
 
-## Pre-trained Models
+# Pre-trained Models
 
 You can download the pre-trained model from [[Google Drive]](https://drive.google.com/file/d/11Ho_seP9NGh-lQCyBkvQhAQFy_3XVwKp/view?usp=drive_link) and then run the test script for fun.
 
-## TETS dataset
+# TETS dataset
 
 Here is the prompts use to generate the coresponding textual informaton of time series via [[OPENAI ChatGPT-3.5 API]](https://platform.openai.com/docs/guides/text-generation)
 
@@ -159,7 +159,7 @@ The time series data are come from [[S&P 500]](https://www.spglobal.com/spdji/en
 
 Example of generated contextual information for the Company marked above:
 
-![Company1_ebitda_summary_words.jpg](pics/Company1_ebitda_summary_words.jpg.png)
+![Company1_ebitda_summary_words.jpg](pics/Company1_ebitda_summary_words.jpg)
 
 
 
@@ -167,7 +167,7 @@ Example of generated contextual information for the Company marked above:
 You can download the processed data with text embedding from GPT2 from: [[TETS]](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link
 ).
 
-## 🚀 News
+# 🚀 News
 
 
 - **Oct 2024**: 🚀 We've streamlined our code structure, enabling users to download the pre-trained model and perform zero-shot inference with a single line of code! Check out our [demo](./run_TEMPO_demo.py) for more details. Our model's download count on HuggingFace is now trackable!
@@ -192,10 +192,10 @@ You can download the processed data with text embedding from GPT2 from: [[TETS]]
 - [] Multimodal pre-training script
 
 
-## Contact
+# Contact
 Feel free to connect [email protected] / [email protected] if you’re interested in applying TEMPO to your real-world application.
 
-## Cite our work
+# Cite our work
 ```
 @inproceedings{
 cao2024tempo,